First, we plot the presence/absence data from the Nishikawa dataset for bonitos.
Very sparse positive sampling points.
Some sampling points off the south of Japan.
No positive sampling points.
Very few positive sampling points east of Australia.
Then, we build the basic tuna model with all the predictors included. Note that the environmental predictors are mean values over 1956-1981.
## [1] "training AUC: 0.9796"
## [1] "testing AUC: 0.922"
Then, we extrapolate for the rest of \(40^{\circ}N\)-\(40^{\circ}S\) and present seasonal distribution maps. The distribution maps are shown side-by-side with the Nishikawa maps.
I feel like this makes stuff up because there are no positive sampling points in this season.
I feel like this makes stuff up because there are no positive sampling points in this season.
For this section, we use Model 1 (full model). First, we build the \(10 \times 10\) grid.
For each season, we associate the \(10 \times 10\) grid with the \(1 \times 1\) grid cells. Then, we limit the area to \(10 \times 10\) grid cells with sampling points.
We only leave \(10 \times 10\) grid cells that have sampling points within a certain % area threshold. We first do this for a more conservative 25% threshold.
We also do it for a more liberal 10% threshold.
Then, we replicate this across the 3 other seasons…